{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 金融借贷数据分析"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "数据集介绍<br>\n",
    "    是一家lending club金融借贷公司的一些数据<br> \n",
    "    存储的是用户小额贷款以及还款的一些信息，数据列很多，大概887379行，74列<br>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 导入模块并设置中文及字符正常显示\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "plt.rcParams['font.sans-serif'] = ['SimHei']\n",
    "plt.rcParams['axes.unicode_minus'] = False "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 设置过滤警告\n",
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>member_id</th>\n",
       "      <th>loan_amnt</th>\n",
       "      <th>funded_amnt</th>\n",
       "      <th>funded_amnt_inv</th>\n",
       "      <th>term</th>\n",
       "      <th>int_rate</th>\n",
       "      <th>installment</th>\n",
       "      <th>grade</th>\n",
       "      <th>sub_grade</th>\n",
       "      <th>...</th>\n",
       "      <th>total_bal_il</th>\n",
       "      <th>il_util</th>\n",
       "      <th>open_rv_12m</th>\n",
       "      <th>open_rv_24m</th>\n",
       "      <th>max_bal_bc</th>\n",
       "      <th>all_util</th>\n",
       "      <th>total_rev_hi_lim</th>\n",
       "      <th>inq_fi</th>\n",
       "      <th>total_cu_tl</th>\n",
       "      <th>inq_last_12m</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1077501</td>\n",
       "      <td>1296599</td>\n",
       "      <td>5000.0</td>\n",
       "      <td>5000.0</td>\n",
       "      <td>4975.0</td>\n",
       "      <td>36 months</td>\n",
       "      <td>10.65</td>\n",
       "      <td>162.87</td>\n",
       "      <td>B</td>\n",
       "      <td>B2</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1077430</td>\n",
       "      <td>1314167</td>\n",
       "      <td>2500.0</td>\n",
       "      <td>2500.0</td>\n",
       "      <td>2500.0</td>\n",
       "      <td>60 months</td>\n",
       "      <td>15.27</td>\n",
       "      <td>59.83</td>\n",
       "      <td>C</td>\n",
       "      <td>C4</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1077175</td>\n",
       "      <td>1313524</td>\n",
       "      <td>2400.0</td>\n",
       "      <td>2400.0</td>\n",
       "      <td>2400.0</td>\n",
       "      <td>36 months</td>\n",
       "      <td>15.96</td>\n",
       "      <td>84.33</td>\n",
       "      <td>C</td>\n",
       "      <td>C5</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1076863</td>\n",
       "      <td>1277178</td>\n",
       "      <td>10000.0</td>\n",
       "      <td>10000.0</td>\n",
       "      <td>10000.0</td>\n",
       "      <td>36 months</td>\n",
       "      <td>13.49</td>\n",
       "      <td>339.31</td>\n",
       "      <td>C</td>\n",
       "      <td>C1</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1075358</td>\n",
       "      <td>1311748</td>\n",
       "      <td>3000.0</td>\n",
       "      <td>3000.0</td>\n",
       "      <td>3000.0</td>\n",
       "      <td>60 months</td>\n",
       "      <td>12.69</td>\n",
       "      <td>67.79</td>\n",
       "      <td>B</td>\n",
       "      <td>B5</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 74 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        id  member_id  loan_amnt  funded_amnt  funded_amnt_inv        term  \\\n",
       "0  1077501    1296599     5000.0       5000.0           4975.0   36 months   \n",
       "1  1077430    1314167     2500.0       2500.0           2500.0   60 months   \n",
       "2  1077175    1313524     2400.0       2400.0           2400.0   36 months   \n",
       "3  1076863    1277178    10000.0      10000.0          10000.0   36 months   \n",
       "4  1075358    1311748     3000.0       3000.0           3000.0   60 months   \n",
       "\n",
       "   int_rate  installment grade sub_grade  ... total_bal_il il_util  \\\n",
       "0     10.65       162.87     B        B2  ...          NaN     NaN   \n",
       "1     15.27        59.83     C        C4  ...          NaN     NaN   \n",
       "2     15.96        84.33     C        C5  ...          NaN     NaN   \n",
       "3     13.49       339.31     C        C1  ...          NaN     NaN   \n",
       "4     12.69        67.79     B        B5  ...          NaN     NaN   \n",
       "\n",
       "  open_rv_12m  open_rv_24m max_bal_bc all_util total_rev_hi_lim inq_fi  \\\n",
       "0         NaN          NaN        NaN      NaN              NaN    NaN   \n",
       "1         NaN          NaN        NaN      NaN              NaN    NaN   \n",
       "2         NaN          NaN        NaN      NaN              NaN    NaN   \n",
       "3         NaN          NaN        NaN      NaN              NaN    NaN   \n",
       "4         NaN          NaN        NaN      NaN              NaN    NaN   \n",
       "\n",
       "  total_cu_tl inq_last_12m  \n",
       "0         NaN          NaN  \n",
       "1         NaN          NaN  \n",
       "2         NaN          NaN  \n",
       "3         NaN          NaN  \n",
       "4         NaN          NaN  \n",
       "\n",
       "[5 rows x 74 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 读取数据并初步查看\n",
    "loanData=pd.read_csv('loan.csv')\n",
    "loanData.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(887379, 74)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 发现数据有 74 列，很多，有些列有很多缺失值\n",
    "loanData.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 887379 entries, 0 to 887378\n",
      "Data columns (total 74 columns):\n",
      " #   Column                       Non-Null Count   Dtype  \n",
      "---  ------                       --------------   -----  \n",
      " 0   id                           887379 non-null  int64  \n",
      " 1   member_id                    887379 non-null  int64  \n",
      " 2   loan_amnt                    887379 non-null  float64\n",
      " 3   funded_amnt                  887379 non-null  float64\n",
      " 4   funded_amnt_inv              887379 non-null  float64\n",
      " 5   term                         887379 non-null  object \n",
      " 6   int_rate                     887379 non-null  float64\n",
      " 7   installment                  887379 non-null  float64\n",
      " 8   grade                        887379 non-null  object \n",
      " 9   sub_grade                    887379 non-null  object \n",
      " 10  emp_title                    835917 non-null  object \n",
      " 11  emp_length                   842554 non-null  object \n",
      " 12  home_ownership               887379 non-null  object \n",
      " 13  annual_inc                   887375 non-null  float64\n",
      " 14  verification_status          887379 non-null  object \n",
      " 15  issue_d                      887379 non-null  object \n",
      " 16  loan_status                  887379 non-null  object \n",
      " 17  pymnt_plan                   887379 non-null  object \n",
      " 18  url                          887379 non-null  object \n",
      " 19  desc                         126028 non-null  object \n",
      " 20  purpose                      887379 non-null  object \n",
      " 21  title                        887227 non-null  object \n",
      " 22  zip_code                     887379 non-null  object \n",
      " 23  addr_state                   887379 non-null  object \n",
      " 24  dti                          887379 non-null  float64\n",
      " 25  delinq_2yrs                  887350 non-null  float64\n",
      " 26  earliest_cr_line             887350 non-null  object \n",
      " 27  inq_last_6mths               887350 non-null  float64\n",
      " 28  mths_since_last_delinq       433067 non-null  float64\n",
      " 29  mths_since_last_record       137053 non-null  float64\n",
      " 30  open_acc                     887350 non-null  float64\n",
      " 31  pub_rec                      887350 non-null  float64\n",
      " 32  revol_bal                    887379 non-null  float64\n",
      " 33  revol_util                   886877 non-null  float64\n",
      " 34  total_acc                    887350 non-null  float64\n",
      " 35  initial_list_status          887379 non-null  object \n",
      " 36  out_prncp                    887379 non-null  float64\n",
      " 37  out_prncp_inv                887379 non-null  float64\n",
      " 38  total_pymnt                  887379 non-null  float64\n",
      " 39  total_pymnt_inv              887379 non-null  float64\n",
      " 40  total_rec_prncp              887379 non-null  float64\n",
      " 41  total_rec_int                887379 non-null  float64\n",
      " 42  total_rec_late_fee           887379 non-null  float64\n",
      " 43  recoveries                   887379 non-null  float64\n",
      " 44  collection_recovery_fee      887379 non-null  float64\n",
      " 45  last_pymnt_d                 869720 non-null  object \n",
      " 46  last_pymnt_amnt              887379 non-null  float64\n",
      " 47  next_pymnt_d                 634408 non-null  object \n",
      " 48  last_credit_pull_d           887326 non-null  object \n",
      " 49  collections_12_mths_ex_med   887234 non-null  float64\n",
      " 50  mths_since_last_major_derog  221703 non-null  float64\n",
      " 51  policy_code                  887379 non-null  float64\n",
      " 52  application_type             887379 non-null  object \n",
      " 53  annual_inc_joint             511 non-null     float64\n",
      " 54  dti_joint                    509 non-null     float64\n",
      " 55  verification_status_joint    511 non-null     object \n",
      " 56  acc_now_delinq               887350 non-null  float64\n",
      " 57  tot_coll_amt                 817103 non-null  float64\n",
      " 58  tot_cur_bal                  817103 non-null  float64\n",
      " 59  open_acc_6m                  21372 non-null   float64\n",
      " 60  open_il_6m                   21372 non-null   float64\n",
      " 61  open_il_12m                  21372 non-null   float64\n",
      " 62  open_il_24m                  21372 non-null   float64\n",
      " 63  mths_since_rcnt_il           20810 non-null   float64\n",
      " 64  total_bal_il                 21372 non-null   float64\n",
      " 65  il_util                      18617 non-null   float64\n",
      " 66  open_rv_12m                  21372 non-null   float64\n",
      " 67  open_rv_24m                  21372 non-null   float64\n",
      " 68  max_bal_bc                   21372 non-null   float64\n",
      " 69  all_util                     21372 non-null   float64\n",
      " 70  total_rev_hi_lim             817103 non-null  float64\n",
      " 71  inq_fi                       21372 non-null   float64\n",
      " 72  total_cu_tl                  21372 non-null   float64\n",
      " 73  inq_last_12m                 21372 non-null   float64\n",
      "dtypes: float64(49), int64(2), object(23)\n",
      "memory usage: 501.0+ MB\n"
     ]
    }
   ],
   "source": [
    "# 查看数据完整性\n",
    "loanData.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# term：代表还款的期限\n",
    "# grade：评级\n",
    "# sub_grade：子级别 \n",
    "# emp_title：职业 \n",
    "# emp_length：工作年限\n",
    "# home_ownership：房屋信息\n",
    "# issue_d：发放贷款的日期\n",
    "# loan_status：贷款的状态,是正在还款还是已经逾期还是无法要回还是在宽限期等等\n",
    "# purpose：贷款的目的\n",
    "# last_pymnt_d：最近一次还款的时间\n",
    "# next_pymnt_d：下一次还款的时间"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 针对数据集缺失值列的意义进行填充或删除\n",
    "# total_rev_hi_lim：总周转高信用/信用额度。以0填充\n",
    "# tot_coll_amt：欠款总额。以0填充\n",
    "# tot_cur_bal：所有帐户的当前总余额。 以0填充\n",
    "# revol_util：循环利用率，或借款人相对于所有可用循环信贷使用的信贷额度。以均值填充\n",
    "# collections_12_mths_ex_med:除医疗账单外12个月的欠款数量。以0填充\n",
    "# inq_last_6mths：过去6个月的征信查询数目（不包括汽车及按揭查询）。以0填充\n",
    "# acc_now_delinq: 借款人现在欠款的账户数量。以0填充\n",
    "# pub_rec: 贬损公共记录的数量。以0填充\n",
    "# open_acc；借款人信用档案中的未结信用额度。以均值填充\n",
    "# total_acc：借款人信用档案中当前信用额度的总数。以均值填充\n",
    "# delinq_2yrs：过去两年借款人信用档案中逾期30天以上的拖欠次数。以0填充\n",
    "# annual_inc:借款人在注册期间自行报告的年收入。以均值填充。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 一、对数据进行基本描述性分析"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>unique</th>\n",
       "      <th>top</th>\n",
       "      <th>freq</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>term</th>\n",
       "      <td>887379</td>\n",
       "      <td>2</td>\n",
       "      <td>36 months</td>\n",
       "      <td>621125</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>grade</th>\n",
       "      <td>887379</td>\n",
       "      <td>7</td>\n",
       "      <td>B</td>\n",
       "      <td>254535</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sub_grade</th>\n",
       "      <td>887379</td>\n",
       "      <td>35</td>\n",
       "      <td>B3</td>\n",
       "      <td>56323</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>emp_title</th>\n",
       "      <td>835917</td>\n",
       "      <td>299271</td>\n",
       "      <td>Teacher</td>\n",
       "      <td>13469</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>emp_length</th>\n",
       "      <td>842554</td>\n",
       "      <td>11</td>\n",
       "      <td>10+ years</td>\n",
       "      <td>291569</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>home_ownership</th>\n",
       "      <td>887379</td>\n",
       "      <td>6</td>\n",
       "      <td>MORTGAGE</td>\n",
       "      <td>443557</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>verification_status</th>\n",
       "      <td>887379</td>\n",
       "      <td>3</td>\n",
       "      <td>Source Verified</td>\n",
       "      <td>329558</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>issue_d</th>\n",
       "      <td>887379</td>\n",
       "      <td>103</td>\n",
       "      <td>Oct-2015</td>\n",
       "      <td>48631</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>loan_status</th>\n",
       "      <td>887379</td>\n",
       "      <td>10</td>\n",
       "      <td>Current</td>\n",
       "      <td>601779</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pymnt_plan</th>\n",
       "      <td>887379</td>\n",
       "      <td>2</td>\n",
       "      <td>n</td>\n",
       "      <td>887369</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>url</th>\n",
       "      <td>887379</td>\n",
       "      <td>887379</td>\n",
       "      <td>https://www.lendingclub.com/browse/loanDetail....</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>desc</th>\n",
       "      <td>126028</td>\n",
       "      <td>124469</td>\n",
       "      <td></td>\n",
       "      <td>246</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>purpose</th>\n",
       "      <td>887379</td>\n",
       "      <td>14</td>\n",
       "      <td>debt_consolidation</td>\n",
       "      <td>524215</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>title</th>\n",
       "      <td>887227</td>\n",
       "      <td>63144</td>\n",
       "      <td>Debt consolidation</td>\n",
       "      <td>414001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>zip_code</th>\n",
       "      <td>887379</td>\n",
       "      <td>935</td>\n",
       "      <td>945xx</td>\n",
       "      <td>9770</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>addr_state</th>\n",
       "      <td>887379</td>\n",
       "      <td>51</td>\n",
       "      <td>CA</td>\n",
       "      <td>129517</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>earliest_cr_line</th>\n",
       "      <td>887350</td>\n",
       "      <td>697</td>\n",
       "      <td>Aug-2001</td>\n",
       "      <td>6659</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>initial_list_status</th>\n",
       "      <td>887379</td>\n",
       "      <td>2</td>\n",
       "      <td>f</td>\n",
       "      <td>456848</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>last_pymnt_d</th>\n",
       "      <td>869720</td>\n",
       "      <td>98</td>\n",
       "      <td>Jan-2016</td>\n",
       "      <td>470150</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>next_pymnt_d</th>\n",
       "      <td>634408</td>\n",
       "      <td>100</td>\n",
       "      <td>Feb-2016</td>\n",
       "      <td>553406</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>last_credit_pull_d</th>\n",
       "      <td>887326</td>\n",
       "      <td>103</td>\n",
       "      <td>Jan-2016</td>\n",
       "      <td>730574</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>application_type</th>\n",
       "      <td>887379</td>\n",
       "      <td>2</td>\n",
       "      <td>INDIVIDUAL</td>\n",
       "      <td>886868</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>verification_status_joint</th>\n",
       "      <td>511</td>\n",
       "      <td>3</td>\n",
       "      <td>Not Verified</td>\n",
       "      <td>283</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                            count  unique  \\\n",
       "term                       887379       2   \n",
       "grade                      887379       7   \n",
       "sub_grade                  887379      35   \n",
       "emp_title                  835917  299271   \n",
       "emp_length                 842554      11   \n",
       "home_ownership             887379       6   \n",
       "verification_status        887379       3   \n",
       "issue_d                    887379     103   \n",
       "loan_status                887379      10   \n",
       "pymnt_plan                 887379       2   \n",
       "url                        887379  887379   \n",
       "desc                       126028  124469   \n",
       "purpose                    887379      14   \n",
       "title                      887227   63144   \n",
       "zip_code                   887379     935   \n",
       "addr_state                 887379      51   \n",
       "earliest_cr_line           887350     697   \n",
       "initial_list_status        887379       2   \n",
       "last_pymnt_d               869720      98   \n",
       "next_pymnt_d               634408     100   \n",
       "last_credit_pull_d         887326     103   \n",
       "application_type           887379       2   \n",
       "verification_status_joint     511       3   \n",
       "\n",
       "                                                                         top  \\\n",
       "term                                                               36 months   \n",
       "grade                                                                      B   \n",
       "sub_grade                                                                 B3   \n",
       "emp_title                                                            Teacher   \n",
       "emp_length                                                         10+ years   \n",
       "home_ownership                                                      MORTGAGE   \n",
       "verification_status                                          Source Verified   \n",
       "issue_d                                                             Oct-2015   \n",
       "loan_status                                                          Current   \n",
       "pymnt_plan                                                                 n   \n",
       "url                        https://www.lendingclub.com/browse/loanDetail....   \n",
       "desc                                                                           \n",
       "purpose                                                   debt_consolidation   \n",
       "title                                                     Debt consolidation   \n",
       "zip_code                                                               945xx   \n",
       "addr_state                                                                CA   \n",
       "earliest_cr_line                                                    Aug-2001   \n",
       "initial_list_status                                                        f   \n",
       "last_pymnt_d                                                        Jan-2016   \n",
       "next_pymnt_d                                                        Feb-2016   \n",
       "last_credit_pull_d                                                  Jan-2016   \n",
       "application_type                                                  INDIVIDUAL   \n",
       "verification_status_joint                                       Not Verified   \n",
       "\n",
       "                             freq  \n",
       "term                       621125  \n",
       "grade                      254535  \n",
       "sub_grade                   56323  \n",
       "emp_title                   13469  \n",
       "emp_length                 291569  \n",
       "home_ownership             443557  \n",
       "verification_status        329558  \n",
       "issue_d                     48631  \n",
       "loan_status                601779  \n",
       "pymnt_plan                 887369  \n",
       "url                             1  \n",
       "desc                          246  \n",
       "purpose                    524215  \n",
       "title                      414001  \n",
       "zip_code                     9770  \n",
       "addr_state                 129517  \n",
       "earliest_cr_line             6659  \n",
       "initial_list_status        456848  \n",
       "last_pymnt_d               470150  \n",
       "next_pymnt_d               553406  \n",
       "last_credit_pull_d         730574  \n",
       "application_type           886868  \n",
       "verification_status_joint     283  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_object=loanData.select_dtypes('object').describe().T\n",
    "data_object"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "887379"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(loanData.term)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "835917"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "loanData.emp_title.count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "887379"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(loanData.emp_title)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>unique</th>\n",
       "      <th>top</th>\n",
       "      <th>freq</th>\n",
       "      <th>missing_pct</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>term</th>\n",
       "      <td>887379</td>\n",
       "      <td>2</td>\n",
       "      <td>36 months</td>\n",
       "      <td>621125</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>grade</th>\n",
       "      <td>887379</td>\n",
       "      <td>7</td>\n",
       "      <td>B</td>\n",
       "      <td>254535</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sub_grade</th>\n",
       "      <td>887379</td>\n",
       "      <td>35</td>\n",
       "      <td>B3</td>\n",
       "      <td>56323</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>emp_title</th>\n",
       "      <td>835917</td>\n",
       "      <td>299271</td>\n",
       "      <td>Teacher</td>\n",
       "      <td>13469</td>\n",
       "      <td>0.057993</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>emp_length</th>\n",
       "      <td>842554</td>\n",
       "      <td>11</td>\n",
       "      <td>10+ years</td>\n",
       "      <td>291569</td>\n",
       "      <td>0.050514</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>home_ownership</th>\n",
       "      <td>887379</td>\n",
       "      <td>6</td>\n",
       "      <td>MORTGAGE</td>\n",
       "      <td>443557</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>verification_status</th>\n",
       "      <td>887379</td>\n",
       "      <td>3</td>\n",
       "      <td>Source Verified</td>\n",
       "      <td>329558</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>issue_d</th>\n",
       "      <td>887379</td>\n",
       "      <td>103</td>\n",
       "      <td>Oct-2015</td>\n",
       "      <td>48631</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>loan_status</th>\n",
       "      <td>887379</td>\n",
       "      <td>10</td>\n",
       "      <td>Current</td>\n",
       "      <td>601779</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pymnt_plan</th>\n",
       "      <td>887379</td>\n",
       "      <td>2</td>\n",
       "      <td>n</td>\n",
       "      <td>887369</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>url</th>\n",
       "      <td>887379</td>\n",
       "      <td>887379</td>\n",
       "      <td>https://www.lendingclub.com/browse/loanDetail....</td>\n",
       "      <td>1</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>desc</th>\n",
       "      <td>126028</td>\n",
       "      <td>124469</td>\n",
       "      <td></td>\n",
       "      <td>246</td>\n",
       "      <td>0.857977</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>purpose</th>\n",
       "      <td>887379</td>\n",
       "      <td>14</td>\n",
       "      <td>debt_consolidation</td>\n",
       "      <td>524215</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>title</th>\n",
       "      <td>887227</td>\n",
       "      <td>63144</td>\n",
       "      <td>Debt consolidation</td>\n",
       "      <td>414001</td>\n",
       "      <td>0.000171</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>zip_code</th>\n",
       "      <td>887379</td>\n",
       "      <td>935</td>\n",
       "      <td>945xx</td>\n",
       "      <td>9770</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>addr_state</th>\n",
       "      <td>887379</td>\n",
       "      <td>51</td>\n",
       "      <td>CA</td>\n",
       "      <td>129517</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>earliest_cr_line</th>\n",
       "      <td>887350</td>\n",
       "      <td>697</td>\n",
       "      <td>Aug-2001</td>\n",
       "      <td>6659</td>\n",
       "      <td>0.000033</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>initial_list_status</th>\n",
       "      <td>887379</td>\n",
       "      <td>2</td>\n",
       "      <td>f</td>\n",
       "      <td>456848</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>last_pymnt_d</th>\n",
       "      <td>869720</td>\n",
       "      <td>98</td>\n",
       "      <td>Jan-2016</td>\n",
       "      <td>470150</td>\n",
       "      <td>0.019900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>next_pymnt_d</th>\n",
       "      <td>634408</td>\n",
       "      <td>100</td>\n",
       "      <td>Feb-2016</td>\n",
       "      <td>553406</td>\n",
       "      <td>0.285077</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>last_credit_pull_d</th>\n",
       "      <td>887326</td>\n",
       "      <td>103</td>\n",
       "      <td>Jan-2016</td>\n",
       "      <td>730574</td>\n",
       "      <td>0.000060</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>application_type</th>\n",
       "      <td>887379</td>\n",
       "      <td>2</td>\n",
       "      <td>INDIVIDUAL</td>\n",
       "      <td>886868</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>verification_status_joint</th>\n",
       "      <td>511</td>\n",
       "      <td>3</td>\n",
       "      <td>Not Verified</td>\n",
       "      <td>283</td>\n",
       "      <td>0.999424</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                            count  unique  \\\n",
       "term                       887379       2   \n",
       "grade                      887379       7   \n",
       "sub_grade                  887379      35   \n",
       "emp_title                  835917  299271   \n",
       "emp_length                 842554      11   \n",
       "home_ownership             887379       6   \n",
       "verification_status        887379       3   \n",
       "issue_d                    887379     103   \n",
       "loan_status                887379      10   \n",
       "pymnt_plan                 887379       2   \n",
       "url                        887379  887379   \n",
       "desc                       126028  124469   \n",
       "purpose                    887379      14   \n",
       "title                      887227   63144   \n",
       "zip_code                   887379     935   \n",
       "addr_state                 887379      51   \n",
       "earliest_cr_line           887350     697   \n",
       "initial_list_status        887379       2   \n",
       "last_pymnt_d               869720      98   \n",
       "next_pymnt_d               634408     100   \n",
       "last_credit_pull_d         887326     103   \n",
       "application_type           887379       2   \n",
       "verification_status_joint     511       3   \n",
       "\n",
       "                                                                         top  \\\n",
       "term                                                               36 months   \n",
       "grade                                                                      B   \n",
       "sub_grade                                                                 B3   \n",
       "emp_title                                                            Teacher   \n",
       "emp_length                                                         10+ years   \n",
       "home_ownership                                                      MORTGAGE   \n",
       "verification_status                                          Source Verified   \n",
       "issue_d                                                             Oct-2015   \n",
       "loan_status                                                          Current   \n",
       "pymnt_plan                                                                 n   \n",
       "url                        https://www.lendingclub.com/browse/loanDetail....   \n",
       "desc                                                                           \n",
       "purpose                                                   debt_consolidation   \n",
       "title                                                     Debt consolidation   \n",
       "zip_code                                                               945xx   \n",
       "addr_state                                                                CA   \n",
       "earliest_cr_line                                                    Aug-2001   \n",
       "initial_list_status                                                        f   \n",
       "last_pymnt_d                                                        Jan-2016   \n",
       "next_pymnt_d                                                        Feb-2016   \n",
       "last_credit_pull_d                                                  Jan-2016   \n",
       "application_type                                                  INDIVIDUAL   \n",
       "verification_status_joint                                       Not Verified   \n",
       "\n",
       "                             freq  missing_pct  \n",
       "term                       621125     0.000000  \n",
       "grade                      254535     0.000000  \n",
       "sub_grade                   56323     0.000000  \n",
       "emp_title                   13469     0.057993  \n",
       "emp_length                 291569     0.050514  \n",
       "home_ownership             443557     0.000000  \n",
       "verification_status        329558     0.000000  \n",
       "issue_d                     48631     0.000000  \n",
       "loan_status                601779     0.000000  \n",
       "pymnt_plan                 887369     0.000000  \n",
       "url                             1     0.000000  \n",
       "desc                          246     0.857977  \n",
       "purpose                    524215     0.000000  \n",
       "title                      414001     0.000171  \n",
       "zip_code                     9770     0.000000  \n",
       "addr_state                 129517     0.000000  \n",
       "earliest_cr_line             6659     0.000033  \n",
       "initial_list_status        456848     0.000000  \n",
       "last_pymnt_d               470150     0.019900  \n",
       "next_pymnt_d               553406     0.285077  \n",
       "last_credit_pull_d         730574     0.000060  \n",
       "application_type           886868     0.000000  \n",
       "verification_status_joint     283     0.999424  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_object['missing_pct']=loanData.apply(lambda x : (len(x)-x.count())/len(x))\n",
    "data_object"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# len(loanData)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# loanData.term.count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 887379 entries, 0 to 887378\n",
      "Data columns (total 74 columns):\n",
      " #   Column                       Non-Null Count   Dtype  \n",
      "---  ------                       --------------   -----  \n",
      " 0   id                           887379 non-null  int64  \n",
      " 1   member_id                    887379 non-null  int64  \n",
      " 2   loan_amnt                    887379 non-null  float64\n",
      " 3   funded_amnt                  887379 non-null  float64\n",
      " 4   funded_amnt_inv              887379 non-null  float64\n",
      " 5   term                         887379 non-null  object \n",
      " 6   int_rate                     887379 non-null  float64\n",
      " 7   installment                  887379 non-null  float64\n",
      " 8   grade                        887379 non-null  object \n",
      " 9   sub_grade                    887379 non-null  object \n",
      " 10  emp_title                    835917 non-null  object \n",
      " 11  emp_length                   842554 non-null  object \n",
      " 12  home_ownership               887379 non-null  object \n",
      " 13  annual_inc                   887375 non-null  float64\n",
      " 14  verification_status          887379 non-null  object \n",
      " 15  issue_d                      887379 non-null  object \n",
      " 16  loan_status                  887379 non-null  object \n",
      " 17  pymnt_plan                   887379 non-null  object \n",
      " 18  url                          887379 non-null  object \n",
      " 19  desc                         126028 non-null  object \n",
      " 20  purpose                      887379 non-null  object \n",
      " 21  title                        887227 non-null  object \n",
      " 22  zip_code                     887379 non-null  object \n",
      " 23  addr_state                   887379 non-null  object \n",
      " 24  dti                          887379 non-null  float64\n",
      " 25  delinq_2yrs                  887350 non-null  float64\n",
      " 26  earliest_cr_line             887350 non-null  object \n",
      " 27  inq_last_6mths               887350 non-null  float64\n",
      " 28  mths_since_last_delinq       433067 non-null  float64\n",
      " 29  mths_since_last_record       137053 non-null  float64\n",
      " 30  open_acc                     887350 non-null  float64\n",
      " 31  pub_rec                      887350 non-null  float64\n",
      " 32  revol_bal                    887379 non-null  float64\n",
      " 33  revol_util                   886877 non-null  float64\n",
      " 34  total_acc                    887350 non-null  float64\n",
      " 35  initial_list_status          887379 non-null  object \n",
      " 36  out_prncp                    887379 non-null  float64\n",
      " 37  out_prncp_inv                887379 non-null  float64\n",
      " 38  total_pymnt                  887379 non-null  float64\n",
      " 39  total_pymnt_inv              887379 non-null  float64\n",
      " 40  total_rec_prncp              887379 non-null  float64\n",
      " 41  total_rec_int                887379 non-null  float64\n",
      " 42  total_rec_late_fee           887379 non-null  float64\n",
      " 43  recoveries                   887379 non-null  float64\n",
      " 44  collection_recovery_fee      887379 non-null  float64\n",
      " 45  last_pymnt_d                 869720 non-null  object \n",
      " 46  last_pymnt_amnt              887379 non-null  float64\n",
      " 47  next_pymnt_d                 634408 non-null  object \n",
      " 48  last_credit_pull_d           887326 non-null  object \n",
      " 49  collections_12_mths_ex_med   887234 non-null  float64\n",
      " 50  mths_since_last_major_derog  221703 non-null  float64\n",
      " 51  policy_code                  887379 non-null  float64\n",
      " 52  application_type             887379 non-null  object \n",
      " 53  annual_inc_joint             511 non-null     float64\n",
      " 54  dti_joint                    509 non-null     float64\n",
      " 55  verification_status_joint    511 non-null     object \n",
      " 56  acc_now_delinq               887350 non-null  float64\n",
      " 57  tot_coll_amt                 817103 non-null  float64\n",
      " 58  tot_cur_bal                  817103 non-null  float64\n",
      " 59  open_acc_6m                  21372 non-null   float64\n",
      " 60  open_il_6m                   21372 non-null   float64\n",
      " 61  open_il_12m                  21372 non-null   float64\n",
      " 62  open_il_24m                  21372 non-null   float64\n",
      " 63  mths_since_rcnt_il           20810 non-null   float64\n",
      " 64  total_bal_il                 21372 non-null   float64\n",
      " 65  il_util                      18617 non-null   float64\n",
      " 66  open_rv_12m                  21372 non-null   float64\n",
      " 67  open_rv_24m                  21372 non-null   float64\n",
      " 68  max_bal_bc                   21372 non-null   float64\n",
      " 69  all_util                     21372 non-null   float64\n",
      " 70  total_rev_hi_lim             817103 non-null  float64\n",
      " 71  inq_fi                       21372 non-null   float64\n",
      " 72  total_cu_tl                  21372 non-null   float64\n",
      " 73  inq_last_12m                 21372 non-null   float64\n",
      "dtypes: float64(49), int64(2), object(23)\n",
      "memory usage: 501.0+ MB\n"
     ]
    }
   ],
   "source": [
    "loanData.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "loanData.dropna(axis=1,\n",
    "               thresh=int(0.75*len(loanData)),\n",
    "               inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 887379 entries, 0 to 887378\n",
      "Data columns (total 52 columns):\n",
      " #   Column                      Non-Null Count   Dtype  \n",
      "---  ------                      --------------   -----  \n",
      " 0   id                          887379 non-null  int64  \n",
      " 1   member_id                   887379 non-null  int64  \n",
      " 2   loan_amnt                   887379 non-null  float64\n",
      " 3   funded_amnt                 887379 non-null  float64\n",
      " 4   funded_amnt_inv             887379 non-null  float64\n",
      " 5   term                        887379 non-null  object \n",
      " 6   int_rate                    887379 non-null  float64\n",
      " 7   installment                 887379 non-null  float64\n",
      " 8   grade                       887379 non-null  object \n",
      " 9   sub_grade                   887379 non-null  object \n",
      " 10  emp_title                   835917 non-null  object \n",
      " 11  emp_length                  842554 non-null  object \n",
      " 12  home_ownership              887379 non-null  object \n",
      " 13  annual_inc                  887375 non-null  float64\n",
      " 14  verification_status         887379 non-null  object \n",
      " 15  issue_d                     887379 non-null  object \n",
      " 16  loan_status                 887379 non-null  object \n",
      " 17  pymnt_plan                  887379 non-null  object \n",
      " 18  url                         887379 non-null  object \n",
      " 19  purpose                     887379 non-null  object \n",
      " 20  title                       887227 non-null  object \n",
      " 21  zip_code                    887379 non-null  object \n",
      " 22  addr_state                  887379 non-null  object \n",
      " 23  dti                         887379 non-null  float64\n",
      " 24  delinq_2yrs                 887350 non-null  float64\n",
      " 25  earliest_cr_line            887350 non-null  object \n",
      " 26  inq_last_6mths              887350 non-null  float64\n",
      " 27  open_acc                    887350 non-null  float64\n",
      " 28  pub_rec                     887350 non-null  float64\n",
      " 29  revol_bal                   887379 non-null  float64\n",
      " 30  revol_util                  886877 non-null  float64\n",
      " 31  total_acc                   887350 non-null  float64\n",
      " 32  initial_list_status         887379 non-null  object \n",
      " 33  out_prncp                   887379 non-null  float64\n",
      " 34  out_prncp_inv               887379 non-null  float64\n",
      " 35  total_pymnt                 887379 non-null  float64\n",
      " 36  total_pymnt_inv             887379 non-null  float64\n",
      " 37  total_rec_prncp             887379 non-null  float64\n",
      " 38  total_rec_int               887379 non-null  float64\n",
      " 39  total_rec_late_fee          887379 non-null  float64\n",
      " 40  recoveries                  887379 non-null  float64\n",
      " 41  collection_recovery_fee     887379 non-null  float64\n",
      " 42  last_pymnt_d                869720 non-null  object \n",
      " 43  last_pymnt_amnt             887379 non-null  float64\n",
      " 44  last_credit_pull_d          887326 non-null  object \n",
      " 45  collections_12_mths_ex_med  887234 non-null  float64\n",
      " 46  policy_code                 887379 non-null  float64\n",
      " 47  application_type            887379 non-null  object \n",
      " 48  acc_now_delinq              887350 non-null  float64\n",
      " 49  tot_coll_amt                817103 non-null  float64\n",
      " 50  tot_cur_bal                 817103 non-null  float64\n",
      " 51  total_rev_hi_lim            817103 non-null  float64\n",
      "dtypes: float64(30), int64(2), object(20)\n",
      "memory usage: 352.0+ MB\n"
     ]
    }
   ],
   "source": [
    "loanData.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "total_rev_hi_lim              70276\n",
       "tot_coll_amt                  70276\n",
       "tot_cur_bal                   70276\n",
       "emp_title                     51462\n",
       "emp_length                    44825\n",
       "last_pymnt_d                  17659\n",
       "revol_util                      502\n",
       "title                           152\n",
       "collections_12_mths_ex_med      145\n",
       "last_credit_pull_d               53\n",
       "total_acc                        29\n",
       "delinq_2yrs                      29\n",
       "inq_last_6mths                   29\n",
       "open_acc                         29\n",
       "pub_rec                          29\n",
       "earliest_cr_line                 29\n",
       "acc_now_delinq                   29\n",
       "annual_inc                        4\n",
       "total_pymnt                       0\n",
       "out_prncp_inv                     0\n",
       "issue_d                           0\n",
       "verification_status               0\n",
       "home_ownership                    0\n",
       "application_type                  0\n",
       "sub_grade                         0\n",
       "grade                             0\n",
       "installment                       0\n",
       "int_rate                          0\n",
       "term                              0\n",
       "funded_amnt_inv                   0\n",
       "funded_amnt                       0\n",
       "loan_amnt                         0\n",
       "member_id                         0\n",
       "loan_status                       0\n",
       "pymnt_plan                        0\n",
       "url                               0\n",
       "total_rec_late_fee                0\n",
       "out_prncp                         0\n",
       "initial_list_status               0\n",
       "total_pymnt_inv                   0\n",
       "total_rec_prncp                   0\n",
       "revol_bal                         0\n",
       "total_rec_int                     0\n",
       "recoveries                        0\n",
       "purpose                           0\n",
       "collection_recovery_fee           0\n",
       "last_pymnt_amnt                   0\n",
       "dti                               0\n",
       "addr_state                        0\n",
       "zip_code                          0\n",
       "policy_code                       0\n",
       "id                                0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "total=loanData.isnull().sum().sort_values(ascending=False)\n",
    "total"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['total_rev_hi_lim', 'tot_coll_amt', 'tot_cur_bal', 'emp_title',\n",
       "       'emp_length', 'last_pymnt_d', 'revol_util', 'title',\n",
       "       'collections_12_mths_ex_med', 'last_credit_pull_d', 'total_acc',\n",
       "       'delinq_2yrs', 'inq_last_6mths', 'open_acc', 'pub_rec',\n",
       "       'earliest_cr_line', 'acc_now_delinq', 'annual_inc'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 缺失值的列名称\n",
    "total[total.values!=0].index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 针对数据集缺失值列的意义进行填充或删除\n",
    "# total_rev_hi_lim：总周转高信用/信用额度。以0填充\n",
    "# tot_coll_amt：欠款总额。以0填充\n",
    "# tot_cur_bal：所有帐户的当前总余额。 以0填充\n",
    "# revol_util：循环利用率，或借款人相对于所有可用循环信贷使用的信贷额度。以均值填充\n",
    "# collections_12_mths_ex_med:除医疗账单外12个月的欠款数量。以0填充\n",
    "# inq_last_6mths：过去6个月的征信查询数目（不包括汽车及按揭查询）。以0填充\n",
    "# acc_now_delinq: 借款人现在欠款的账户数量。以0填充\n",
    "# pub_rec: 贬损公共记录的数量。以0填充\n",
    "# open_acc；借款人信用档案中的未结信用额度。以均值填充\n",
    "# total_acc：借款人信用档案中当前信用额度的总数。以均值填充\n",
    "# delinq_2yrs：过去两年借款人信用档案中逾期30天以上的拖欠次数。以0填充\n",
    "# annual_inc:借款人在注册期间自行报告的年收入。以均值填充。\n",
    "\n",
    "# 按照 0 填充的列定义为列表\n",
    "cols_fill_with_zero = [\n",
    "    'total_rev_hi_lim', 'tot_cur_bal', 'tot_coll_amt',\n",
    "    'collections_12_mths_ex_med', 'inq_last_6mths', 'acc_now_delinq',\n",
    "    'pub_rec', 'delinq_2yrs'\n",
    "]\n",
    "# 按照 均值 填充的列定义为列表\n",
    "cols_fill_with_mean = ['revol_util'\n",
    "                       , 'open_acc'\n",
    "                       , 'total_acc'\n",
    "                       , 'annual_inc']\n",
    "\n",
    "# 填补0\n",
    "for col in cols_fill_with_zero:\n",
    "    loanData[col]=loanData[col].fillna(0)\n",
    "\n",
    "# 填补为均值\n",
    "for col in cols_fill_with_mean:\n",
    "    mean_of_col=loanData[col].mean()\n",
    "    loanData[col]=loanData[col].fillna(mean_of_col)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "total=loanData.isnull().sum().sort_values(ascending=False)\n",
    "# total"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "loanData.dropna(axis=0,inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(819022, 52)"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "loanData.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 819022 entries, 1 to 887378\n",
      "Data columns (total 52 columns):\n",
      " #   Column                      Non-Null Count   Dtype  \n",
      "---  ------                      --------------   -----  \n",
      " 0   id                          819022 non-null  int64  \n",
      " 1   member_id                   819022 non-null  int64  \n",
      " 2   loan_amnt                   819022 non-null  float64\n",
      " 3   funded_amnt                 819022 non-null  float64\n",
      " 4   funded_amnt_inv             819022 non-null  float64\n",
      " 5   term                        819022 non-null  object \n",
      " 6   int_rate                    819022 non-null  float64\n",
      " 7   installment                 819022 non-null  float64\n",
      " 8   grade                       819022 non-null  object \n",
      " 9   sub_grade                   819022 non-null  object \n",
      " 10  emp_title                   819022 non-null  object \n",
      " 11  emp_length                  819022 non-null  object \n",
      " 12  home_ownership              819022 non-null  object \n",
      " 13  annual_inc                  819022 non-null  float64\n",
      " 14  verification_status         819022 non-null  object \n",
      " 15  issue_d                     819022 non-null  object \n",
      " 16  loan_status                 819022 non-null  object \n",
      " 17  pymnt_plan                  819022 non-null  object \n",
      " 18  url                         819022 non-null  object \n",
      " 19  purpose                     819022 non-null  object \n",
      " 20  title                       819022 non-null  object \n",
      " 21  zip_code                    819022 non-null  object \n",
      " 22  addr_state                  819022 non-null  object \n",
      " 23  dti                         819022 non-null  float64\n",
      " 24  delinq_2yrs                 819022 non-null  float64\n",
      " 25  earliest_cr_line            819022 non-null  object \n",
      " 26  inq_last_6mths              819022 non-null  float64\n",
      " 27  open_acc                    819022 non-null  float64\n",
      " 28  pub_rec                     819022 non-null  float64\n",
      " 29  revol_bal                   819022 non-null  float64\n",
      " 30  revol_util                  819022 non-null  float64\n",
      " 31  total_acc                   819022 non-null  float64\n",
      " 32  initial_list_status         819022 non-null  object \n",
      " 33  out_prncp                   819022 non-null  float64\n",
      " 34  out_prncp_inv               819022 non-null  float64\n",
      " 35  total_pymnt                 819022 non-null  float64\n",
      " 36  total_pymnt_inv             819022 non-null  float64\n",
      " 37  total_rec_prncp             819022 non-null  float64\n",
      " 38  total_rec_int               819022 non-null  float64\n",
      " 39  total_rec_late_fee          819022 non-null  float64\n",
      " 40  recoveries                  819022 non-null  float64\n",
      " 41  collection_recovery_fee     819022 non-null  float64\n",
      " 42  last_pymnt_d                819022 non-null  object \n",
      " 43  last_pymnt_amnt             819022 non-null  float64\n",
      " 44  last_credit_pull_d          819022 non-null  object \n",
      " 45  collections_12_mths_ex_med  819022 non-null  float64\n",
      " 46  policy_code                 819022 non-null  float64\n",
      " 47  application_type            819022 non-null  object \n",
      " 48  acc_now_delinq              819022 non-null  float64\n",
      " 49  tot_coll_amt                819022 non-null  float64\n",
      " 50  tot_cur_bal                 819022 non-null  float64\n",
      " 51  total_rev_hi_lim            819022 non-null  float64\n",
      "dtypes: float64(30), int64(2), object(20)\n",
      "memory usage: 331.2+ MB\n"
     ]
    }
   ],
   "source": [
    "loanData.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 对数据进行探索性分析"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Current                                                558269\n",
       "Fully Paid                                             197119\n",
       "Charged Off                                             41288\n",
       "Late (31-120 days)                                      10683\n",
       "In Grace Period                                          5778\n",
       "Late (16-30 days)                                        2155\n",
       "Does not meet the credit policy. Status:Fully Paid       1862\n",
       "Default                                                  1131\n",
       "Does not meet the credit policy. Status:Charged Off       697\n",
       "Issued                                                     40\n",
       "Name: loan_status, dtype: int64"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 划分好账和坏账\n",
    "loanData.loan_status.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Current:正在还款\n",
    "# Fully Paid:全额付清\n",
    "# Charged Off：有违约\n",
    "# Late (31-120 days):逾期\n",
    "# In Grace Period ：在宽限期\n",
    "# Late (16-30 days) ：逾期\n",
    "# Does not meet the credit policy. Status:Fully Paid ：全额付清\n",
    "# Default：坏账\n",
    "# Issued:刚发放"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "#简化贷款质量\n",
    "good_loan=[\n",
    "    'Current','Fully Paid',\n",
    "    'Does not meet the credit policy. Status:Fully Paid',\n",
    "    'Issued'\n",
    "]\n",
    "def loan_condition(status):\n",
    "    if status in good_loan:\n",
    "        return 'good_loan'\n",
    "    else:\n",
    "        return 'bad_loan'\n",
    "# 新增一列 贷款质量    \n",
    "loanData['loan_condition']=loanData.loan_status.apply(loan_condition)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "good_loan    757290\n",
       "bad_loan      61732\n",
       "Name: loan_condition, dtype: int64"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "loanData['loan_condition'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "# loanData['issue_d']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "loanData['issue_d']=pd.to_datetime(loanData['issue_d'])\n",
    "# loanData['issue_d']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>member_id</th>\n",
       "      <th>loan_amnt</th>\n",
       "      <th>funded_amnt</th>\n",
       "      <th>funded_amnt_inv</th>\n",
       "      <th>term</th>\n",
       "      <th>int_rate</th>\n",
       "      <th>installment</th>\n",
       "      <th>grade</th>\n",
       "      <th>sub_grade</th>\n",
       "      <th>...</th>\n",
       "      <th>collections_12_mths_ex_med</th>\n",
       "      <th>policy_code</th>\n",
       "      <th>application_type</th>\n",
       "      <th>acc_now_delinq</th>\n",
       "      <th>tot_coll_amt</th>\n",
       "      <th>tot_cur_bal</th>\n",
       "      <th>total_rev_hi_lim</th>\n",
       "      <th>loan_condition</th>\n",
       "      <th>issue_year</th>\n",
       "      <th>issue_month</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1077430</td>\n",
       "      <td>1314167</td>\n",
       "      <td>2500.0</td>\n",
       "      <td>2500.0</td>\n",
       "      <td>2500.0</td>\n",
       "      <td>60 months</td>\n",
       "      <td>15.27</td>\n",
       "      <td>59.83</td>\n",
       "      <td>C</td>\n",
       "      <td>C4</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>INDIVIDUAL</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>bad_loan</td>\n",
       "      <td>2011</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1076863</td>\n",
       "      <td>1277178</td>\n",
       "      <td>10000.0</td>\n",
       "      <td>10000.0</td>\n",
       "      <td>10000.0</td>\n",
       "      <td>36 months</td>\n",
       "      <td>13.49</td>\n",
       "      <td>339.31</td>\n",
       "      <td>C</td>\n",
       "      <td>C1</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>INDIVIDUAL</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>good_loan</td>\n",
       "      <td>2011</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1075358</td>\n",
       "      <td>1311748</td>\n",
       "      <td>3000.0</td>\n",
       "      <td>3000.0</td>\n",
       "      <td>3000.0</td>\n",
       "      <td>60 months</td>\n",
       "      <td>12.69</td>\n",
       "      <td>67.79</td>\n",
       "      <td>B</td>\n",
       "      <td>B5</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>INDIVIDUAL</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>good_loan</td>\n",
       "      <td>2011</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1075269</td>\n",
       "      <td>1311441</td>\n",
       "      <td>5000.0</td>\n",
       "      <td>5000.0</td>\n",
       "      <td>5000.0</td>\n",
       "      <td>36 months</td>\n",
       "      <td>7.90</td>\n",
       "      <td>156.46</td>\n",
       "      <td>A</td>\n",
       "      <td>A4</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>INDIVIDUAL</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>good_loan</td>\n",
       "      <td>2011</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1069639</td>\n",
       "      <td>1304742</td>\n",
       "      <td>7000.0</td>\n",
       "      <td>7000.0</td>\n",
       "      <td>7000.0</td>\n",
       "      <td>60 months</td>\n",
       "      <td>15.96</td>\n",
       "      <td>170.08</td>\n",
       "      <td>C</td>\n",
       "      <td>C5</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>INDIVIDUAL</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>good_loan</td>\n",
       "      <td>2011</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 55 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        id  member_id  loan_amnt  funded_amnt  funded_amnt_inv        term  \\\n",
       "1  1077430    1314167     2500.0       2500.0           2500.0   60 months   \n",
       "3  1076863    1277178    10000.0      10000.0          10000.0   36 months   \n",
       "4  1075358    1311748     3000.0       3000.0           3000.0   60 months   \n",
       "5  1075269    1311441     5000.0       5000.0           5000.0   36 months   \n",
       "6  1069639    1304742     7000.0       7000.0           7000.0   60 months   \n",
       "\n",
       "   int_rate  installment grade sub_grade  ... collections_12_mths_ex_med  \\\n",
       "1     15.27        59.83     C        C4  ...                        0.0   \n",
       "3     13.49       339.31     C        C1  ...                        0.0   \n",
       "4     12.69        67.79     B        B5  ...                        0.0   \n",
       "5      7.90       156.46     A        A4  ...                        0.0   \n",
       "6     15.96       170.08     C        C5  ...                        0.0   \n",
       "\n",
       "  policy_code application_type  acc_now_delinq tot_coll_amt tot_cur_bal  \\\n",
       "1         1.0       INDIVIDUAL             0.0          0.0         0.0   \n",
       "3         1.0       INDIVIDUAL             0.0          0.0         0.0   \n",
       "4         1.0       INDIVIDUAL             0.0          0.0         0.0   \n",
       "5         1.0       INDIVIDUAL             0.0          0.0         0.0   \n",
       "6         1.0       INDIVIDUAL             0.0          0.0         0.0   \n",
       "\n",
       "  total_rev_hi_lim loan_condition issue_year issue_month  \n",
       "1              0.0       bad_loan       2011          12  \n",
       "3              0.0      good_loan       2011          12  \n",
       "4              0.0      good_loan       2011          12  \n",
       "5              0.0      good_loan       2011          12  \n",
       "6              0.0      good_loan       2011          12  \n",
       "\n",
       "[5 rows x 55 columns]"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 增加年月两列数据\n",
    "loanData['issue_year']=loanData['issue_d'].apply(lambda x:x.year)\n",
    "loanData['issue_month']=loanData['issue_d'].apply(lambda x:x.month)\n",
    "loanData.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "f1,(ax1,ax2)=plt.subplots(1,2,figsize=(16,6))\n",
    "# 贷款质量评估\n",
    "loanData.loan_condition.value_counts().plot(kind='pie',\n",
    "                            autopct='%.2f%%',\n",
    "                            ax=ax1,\n",
    "                            fontsize=16)\n",
    "ax1.set_title('贷款质量评估')\n",
    "# 年度好坏账分布情况  2007-2015\n",
    "# 比如  100%  2007年好账 坏账各自比例\n",
    "sns.barplot(x='issue_year'\n",
    "            ,data=loanData,\n",
    "           hue='loan_condition',\n",
    "           y='loan_amnt',\n",
    "           estimator=lambda x: len(x)/len(loanData)*100,\n",
    "           ax=ax2)\n",
    "ax2.set_title('年度好坏账分布情况')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2007"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "loanData.issue_year.min()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2015"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "loanData.issue_year.max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 贷款人数量年度分布\n",
    "f2,(ax1,ax2)=plt.subplots(1,2,figsize=(16,6))\n",
    "day_dist=loanData.groupby('issue_d').size()\n",
    "day_dist.plot(ax=ax1)\n",
    "ax1.set_title('贷款人数量详细分布')\n",
    "\n",
    "year_dist=loanData.groupby('issue_year').size()\n",
    "year_dist.plot(ax=ax2)\n",
    "ax2.set_title('贷款人数量年度分布')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 客户画像\n",
    "# 地域分布\n",
    "plt.figure(figsize=(10,6))\n",
    "loanData.addr_state.value_counts().head(10).plot(kind='barh')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "CA    120045\n",
       "NY     68362\n",
       "TX     66349\n",
       "FL     55255\n",
       "IL     32979\n",
       "NJ     31095\n",
       "PA     29141\n",
       "OH     27269\n",
       "GA     26773\n",
       "VA     24510\n",
       "NC     22575\n",
       "MI     20582\n",
       "MD     19606\n",
       "MA     19406\n",
       "AZ     18539\n",
       "WA     17949\n",
       "CO     17534\n",
       "MN     14956\n",
       "MO     13097\n",
       "IN     12762\n",
       "CT     12598\n",
       "TN     11796\n",
       "NV     11176\n",
       "WI     10714\n",
       "AL     10201\n",
       "OR      9868\n",
       "LA      9789\n",
       "SC      9638\n",
       "KY      7818\n",
       "OK      7429\n",
       "KS      7319\n",
       "AR      5978\n",
       "UT      5904\n",
       "NM      4440\n",
       "HI      4267\n",
       "NH      4024\n",
       "WV      3956\n",
       "RI      3659\n",
       "MS      3474\n",
       "MT      2337\n",
       "DC      2317\n",
       "DE      2285\n",
       "AK      2075\n",
       "WY      1912\n",
       "SD      1700\n",
       "VT      1653\n",
       "NE      1063\n",
       "ND       415\n",
       "ME       409\n",
       "IA        14\n",
       "ID        10\n",
       "Name: addr_state, dtype: int64"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "loanData.addr_state.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f61e7cded30>"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 分析职业主要分布 emp_title\n",
    "loanData.emp_title.value_counts().head(20).plot(kind='barh')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 800x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 分析工作年限 emp_length\n",
    "plt.figure(figsize=(8,8),dpi=100)\n",
    "loanData.emp_length.value_counts().plot(kind='pie',\n",
    "                                       autopct='%.1f%%')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 600x600 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 分析用户年收入 annual_inc\n",
    "import seaborn as sns\n",
    "plt.figure(figsize=(6,6),dpi=100)\n",
    "sns.distplot(loanData.annual_inc)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    8.190220e+05\n",
       "mean     7.614336e+04\n",
       "std      6.525442e+04\n",
       "min      1.896000e+03\n",
       "25%      4.700000e+04\n",
       "50%      6.500000e+04\n",
       "75%      9.000000e+04\n",
       "max      9.500000e+06\n",
       "Name: annual_inc, dtype: float64"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "loanData.annual_inc.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "def inc_type(income):\n",
    "    if income <=20000:\n",
    "        return 'low'\n",
    "    elif income>20000 and income<=60000:\n",
    "        return 'middle'\n",
    "    else:\n",
    "        return 'high'\n",
    "loanData['inc_type']=loanData.annual_inc.apply(inc_type)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "loanData['inc_type'].value_counts().plot(kind='bar',\n",
    "                                        rot=0)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f61d745d898>"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 分析住房类型  home_ownership\n",
    "loanData['home_ownership'].value_counts().plot(kind='bar',\n",
    "                                              rot=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f61e7a3d6d8>"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "loanData['home_ownership'].value_counts().head(3).plot(kind='pie',\n",
    "                                                      autopct='%.2f%%')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f61e7a0dfd0>"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 分析贷款目的 purpose\n",
    "plt.figure(figsize=(8,6))\n",
    "sns.countplot(y=loanData.purpose) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 债务整合 信用卡还款"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 分析贷款金额分布 loan_amnt\n",
    "plt.figure(figsize=(8,6))\n",
    "sns.distplot(loanData.loan_amnt)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 分析借款周期 term\n",
    "loanData.term.value_counts().plot(kind='pie',\n",
    "                                 autopct='%.2f%%')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 每月还款占月收入的比例 dti\n",
    "plt.figure(figsize=(8,6))\n",
    "sns.distplot(loanData.dti)\n",
    "plt.xlim((0,50))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 平均贷款利率与信用等级关系、\n",
    "# 贷款情况与信用等级的关系"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f61e761f748>"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "f3,(ax1,ax2)=plt.subplots(1,2,figsize=(20,6))\n",
    "groupby_grade=loanData.groupby('grade')\n",
    "groupby_grade['int_rate'].mean().plot(kind='bar',\n",
    "                                     ax=ax1)\n",
    "\n",
    "sns.countplot(data=loanData,\n",
    "             x='grade',\n",
    "             hue='loan_condition',\n",
    "             ax=ax2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "# loanData['inc_type']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f61e7591eb8>"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(data=loanData,\n",
    "             x='inc_type',\n",
    "             hue='loan_condition')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>loan_condition</th>\n",
       "      <th>bad_loan</th>\n",
       "      <th>good_loan</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>inc_type</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>high</th>\n",
       "      <td>28353</td>\n",
       "      <td>417967</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>low</th>\n",
       "      <td>845</td>\n",
       "      <td>7222</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>middle</th>\n",
       "      <td>32534</td>\n",
       "      <td>332101</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "loan_condition  bad_loan  good_loan\n",
       "inc_type                           \n",
       "high               28353     417967\n",
       "low                  845       7222\n",
       "middle             32534     332101"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "inc_loan=pd.crosstab(index=loanData.inc_type,\n",
    "           columns=loanData.loan_condition)\n",
    "inc_loan"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>loan_condition</th>\n",
       "      <th>bad_loan</th>\n",
       "      <th>good_loan</th>\n",
       "      <th>坏账概率</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>inc_type</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>high</th>\n",
       "      <td>28353</td>\n",
       "      <td>417967</td>\n",
       "      <td>0.063526</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>low</th>\n",
       "      <td>845</td>\n",
       "      <td>7222</td>\n",
       "      <td>0.104748</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>middle</th>\n",
       "      <td>32534</td>\n",
       "      <td>332101</td>\n",
       "      <td>0.089223</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "loan_condition  bad_loan  good_loan      坏账概率\n",
       "inc_type                                     \n",
       "high               28353     417967  0.063526\n",
       "low                  845       7222  0.104748\n",
       "middle             32534     332101  0.089223"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "inc_loan['坏账概率']=inc_loan['bad_loan']/(inc_loan['bad_loan']+inc_loan['good_loan'])\n",
    "inc_loan"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "项目基于某贷款公司业务数据及客户数据\n",
    "进行多维度的分析，主要完成以下内容：\n",
    "\n",
    "1、数据预处理：数据为88万行，74列，完整性较差，根据数据缺失情况及业务方要求使用删除法和替换等方法对于数据进行一致性处理，保证数据业务的完整度及可分析性\n",
    "2、贷款业务分析：简化贷款质量特征，根据业务划分好账坏账，分析年度好坏账分布变化情况，并通过贷款人数量及金额趋势变化分析公司业务开展情况，多维度分析客户产生坏账的原因。方便之后进行风险控制。\n",
    "3、客户画像分析：通过分析现有客户群体的地域，职业，工作年限，年收入，住房类型，贷款目的，贷款金额，借款周期等分布情况，定义目标客户群体，为后期渠道的推广营销做决策支持。"
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